% Template Copyright (C) 2014 by Thomas Auzinger <thomas.auzinger@cg.tuwien.ac.at>

\documentclass[final]{vutinfth} % Remove option 'final' to obtain debug information. draft,

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\usepackage[ruled,linesnumbered,algochapter]{algorithm2e} % Enables the writing of pseudo code.
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\setsecnumdepth{subsection} % enumerate subsections

% Use an optional index
\makeindex
% Use an optional glossary
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%\glstocfalse % remove the glossaries from the table of contents

% Set persons with 4 arguments:
%  {title before name}{name}{title after name}{gender}
%  where both titles are optional (i.e. can be given as empty brackets {})
\setauthor{}{Martin Unger}{BSc}{male}
\setadvisor{Ao. Univ.-Prof. Mag. Dr.}{Horst Eidenberger}{}{male}

% For bachelor and master theses
%\setfirstassistant{Pretitle}{Forename Surname}{Posttitle}{male}
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% For dissertations
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% Required data
\setaddress{Alliiertenstra{\ss}e 5/17, 1020 Wien}
\setregnumber{0726109}
\setdate{04}{03}{2015}
\settitle{Machine Learning With Dual Process Models}{Machine Learning With Dual Process Models} % sets English and German version of the title (both can be English or German)
\setsubtitle{}{} % sets English and German version of the subtitle (both can be English or German)

% Select the thesis type: bachelor / master / doctor
% Bachelor:
%\setthesis{bachelor}
%
% Master:
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%
% Doctor:
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%\setdoctordegree{rer.soc.oec.}% rer.nat. / techn. / rer.soc.oec.

% For bachelor and master
\setcurriculum{Business Informatics}{Wirtschaftsinformatik} % sets the English and German name of the curriculum


\begin{document}

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% The structure of the thesis has to conform to
%  http://www.informatik.tuwien.ac.at/dekanat

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\addtitlepage{english} % English title page

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\mainmatter


\begin{kurzfassung*}
{\"A}hnlichkeitsmessung ist ein wichtiger Bestandteil der meisten Maschinenlern-Algorithmen. Traditionelle Ans{\"a}tze richten ihr Hauptaugenmerk entweder auf taxonomisches oder thematisches Denken. Psychologische Forschung deutet aber darauf hin, dass eine Kombination beider Ans{\"a}tze n{\"o}tig ist, um eine menschen{\"a}hnliche {\"A}hnlichkeitsmessung zu erreichen. Diese Kombination nennt man Similarity Dual Process Model (DPM).

Diese Arbeit beschreibt, wie ein DPM als lineare Kombination von Distanz- und {\"A}hnlichkeitsma{\ss}en erzeugt werden kann. Wir nutzen Generalisierungsfunktionen, um Distanz in {\"A}hnlichkeit umzuwandeln. DPMs sind Kernelfunktionen {\"a}hnlich. Deshalb k{\"o}nnen sie in jeden Maschinenlern-Algorithmus, der Kernelfunktionen nutzt, integriert werden. Um die Verwendung von DPMs zu f{\"o}rdern, stellen wir  Implementierungen von Kernelfunktionen zur Verf{\"u}gung.

Nat{\"u}rlich funktionieren nicht alle DPMs, die wir formulieren k{\"o}nnen, gleich gut. Deshalb testen wir die Leistung mit einer praktischen Anwendung: der Erkennung von Fu{\ss}g{\"a}ngern in Bildern. Wir nehmen an, dass DPMs nur sinnvoll sind, sofern ihre Leistung als Ganzes besser ist als die ihrer Teile. In den Experimenten haben wir DPM-Kernel gefunden, die f{\"u}r die Testdaten eine mit konventionellen Kerneln vergleichbare Leistung erbracht haben. Wir stellen daher einen Baukasten zum Formulieren solcher Kernel bereit, um weitere Experimente in anderen Anwendungsbereichen des Maschinenlernens zu unterst{\"u}tzen.


\end{kurzfassung*}

\begin{abstract*}

Similarity measurement processes are a core part of most machine learning algorithms. Traditional approaches focus on either taxonomic or thematic thinking. Psychological research suggests that a combination of both is needed to model human-like similarity perception adequately. Such a combination is called a Similarity Dual Process Model (DPM).

This thesis describes how to construct DPMs as a linear combination of existing measures of similarity and distance. We use generalization functions to convert distance into similarity. DPMs are similar to kernel functions. Thus, they can be integrated into any machine learning algorithm that uses kernel functions. To foster the use of DPMs, we provide kernel function implementations.

Clearly, not all DPMs that can be formulated work equally well. Therefore, we test classification performance in a real-world task: the detection of pedestrians in images. We assume that DPMs are only viable if they are better classifiers than their constituting parts. In our experiments, we found DPM kernels that matched the performance of conventional kernels for our data set. Eventually, we provide a construction kit to build such kernels to encourage further experiments in other application domains of machine learning.

\end{abstract*}

% Select the language of the thesis, e.g., english or naustrian.
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% Add a table of contents (toc)
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% Use an optional list of tables
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% Use an optional list of alogrithms
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% Switch to arabic numbering and start the enumeration of chapters in the table of content.
%\mainmatter

\include{introduction}
\include{background}
\include{implementation}
\include{results}
\include{conclusion}

\backmatter

\begin{appendices}
\include{appendix}
\end{appendices}

% Add a bibliography
\bibliographystyle{alpha}
\bibliography{thesis}

% Add an index
\printindex

% Add a glossary
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\end{document}
